黄勇,,
董云龙,
何友,
陈小龙
海军航空大学 ??烟台 ??264001
基金项目:国家自然科学基金(U1633122, 61871391, 61471382, 61531020, 61671462)，国防科技基金(2102024)，中国科协“青年人才托举工程”专项经费(YESS20160115)
详细信息
作者简介:裴家正(1994–)，男，河南郑州人，海军航空大学博士研究生，主要研究方向为雷达弱小目标检测前跟踪。E-mail: roycerover@163.com
黄勇：黄 勇(1979–)，男，湖南汨罗人，海军航空大学副教授，主要研究方向为MIMO雷达目标检测算法等。E-mail: huangyong_2003@163.com
董云龙(1974–)，男，天津宝坻人，海军航空大学副研究员，主要研究方向为雷达组网、多传感器信息融合。E-mail: china_dyl@sina.com
何友：何 友(1956–)，男，吉林磐石人，中国工程院院士，主要研究方向为雷达子适应检测方法、多传感信息融合、多目标跟踪、分布检测理论及应用、系统仿真与作战模拟等。E-mail: heyouhjhy@126.com
陈小龙(1985–)，男，山东烟台人，海军航空大学副教授，主要研究方向为雷达动目标检测、海杂波抑制、雷达信号精细化处理等。E-mail: cxlcxl1203@163.com
通讯作者:黄勇 ?huangyong_2003@163.com
中图分类号:TN953; TN957计量
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被引次数:0
出版历程
收稿日期:2018-08-23
修回日期:2018-11-05
网络出版日期:2019-01-10
Track-Before-Detect Algorithm Based on Improved Auxiliary Particle PHD Filter under Clutter Background
PEI Jiazheng,HUANG Yong,,
DONG Yunlong,
HE You,
CHEN Xiaolong
Naval Aviation University, Yantai 264001, China
Funds:The National Natural Science Foundation of China (U1633122, 61871391, 61471382, 61531020, 61671462), National Defense Science Foundation (2102024), Young Elite Scientist Sponsorship Program of CAST (YESS20160115)
More Information
Corresponding author:HUANG Yong, huangyong_2003@163.com
摘要
摘要:在杂波背景条件下，现有的基于概率假设密度(PHD)滤波的粒子滤波检测前跟踪(TBD)算法，存在对密集多目标数目估计不准，使用粒子数目较多会造成维数灾难的问题。因此，该文引入两层粒子的概念，将基于平行分割(PP)理论的辅助粒子滤波(APF)应用于基于概率假设密度的检测前跟踪 (PHD-TBD)算法中，提出基于概率假设密度滤波的平行分割辅助粒子滤波检测前跟踪(APP-PF-PHD-TBD)算法以提高目标数目及状态估计精度。仿真实验证明，相对于现有基于PHD的粒子滤波检测前跟踪算法，该算法在目标数目和状态估计精度上具有显著的性能优势，在密集目标场景下，优势尤为突出。最后，利用导航雷达实测所得海杂波背景数据证明，该算法在应用中性能更加优异。
关键词:平行分割/
辅助粒子滤波/
概率假设密度/
检测前跟踪/
随机有限集
Abstract:Under the clutter background condition, the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density (PHD) filtering is not accurate enough to estimate the number of targets in dense multi-objectives. In this study, the concept of two-layer particle is introduced. The Auxiliary Particle Filter (APF) based on Parallel Partition (PP) theory is applied to PHD-TBD. The Auxiliary Parallel Partition Particle Filter (which is based on APF and PP) Track-Before-Detect based on the Probability Hypothesis Density filter (APP-PF-PHD-TBD) algorithm is proposed to improve the target number and state estimation accuracy. The simulation results show that, compared with the existing PHD-filtering-based particle filter track-before-detect algorithm, the proposed algorithm has significant performance advantages in target number and state estimation accuracy. These advantages are particularly obvious in dense target scenarios. Finally, the sea clutter background data obtained using the navigation radar prove that the proposed algorithm outperforms the existing PHD-filtering-based particle filter track-before-detect algorithm in application.
Key words:Parallel Partition (PP)/
Auxiliary Particle Filter (APF)/
Probability Hypothesis Density (PHD)/
Track-Before-Detect (TBD)/
Random Finite Set (RFS)
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